Longitudinal automatic carrier landing system guidance law using model predictive control with an additional landing risk term

Abstract

This paper used a model predictive control with an additional term to develop a modified longitudinal guidance law to reduce landing risk in an automatic carrier landing system. The landing risk model was established by using a longitudinal trajectory and touchdown point predictive principle. A traditional MPC was then involved in designing a modified automatic carrier landing system guidance law for the proposed model. The nonlinear landing mathematic model of an F/A-18 carrier-based aircraft was initially established. Considering the processed procedure in the model predictive control algorithm, the corresponding linear landing model was derived on the basis of the equilibrium states of the F/A-18. Second, landing trajectory in the longitudinal plane was analysed so that the predictive principle of the trajectory trend was reasonably addressed. Depending on the experimental sample data of a pilot model, some linear imitating envelopes are transformed from the corresponding nonlinear trajectory clusters. Furthermore, a touchdown point prediction model was further established based on the predicted trajectory and touchdown point. Third, the traditional model predictive control was introduced to integrate the landing risk term in the performance cost function to develop a novel modified algorithm that not only guides the aircraft to automatically approach and land on the carrier, but also eliminates landing risk during the final carrier approach. Linear matrix inequalities were imported to substitute algebraic inequalities derived from this new algorithm to increase calculating speed. A simulation mission was conducted on a semi-physical platform and compared with the traditional model predictive control without the additional term. The theoretical results validated the correctness and robustness of the modified algorithm and its capability to eliminate landing risk during terminal carrier approach.

Keywords

Automatic carrier landing system trajectory clusters prediction landing risk model model predictive control linear matrix inequalities

Get full access to this article

View all access options for this article.

References

Dai

Tian

Rong

et al.

Hybrid safety analysis method based on SVM and RST: an application to carrier landing of aircraft. Saf Sci 2015; 80: 56–65. DOI: 10.1016/j.ssci.2015.07.006.

Urnes JM and Hess RK. Integrated flight control systems development – the F/A-18A automatic carrier landing system. In: AIAA guidance and control conference, Washington, DC, USA, 1983, AIAA-83-2162, pp.920–929.

Urnes JM, Hess RK, Moomaw RF, et al. Development of the Navy H-Dot Automatic Carrier Landing System designed to give improved approach control in air turbulence. In: AIAA guidance and control conference, Washington, DC, USA, 1979, AIAA-79-1772, pp.491–501.

Steinberg M. A fuzzy logic based F/A-18 automatic carrier landing system. In: AIAA guidance, navigation and control conference. Washington, DC, USA, 1992, AIAA-92-4392, pp.407–417.

Lungu

. Automatic control of aircraft lateral-directional motion during landing using neural networks and radio-technical subsystems. Neurocomputing 2016; 171: 471–481. DOI: 10.1016/j.neucom.2015.06.084.

Rao

. Automatic landing system design using sliding mode control. Aerosp Sci Technol 2014; 32: 180–187. DOI: 10.1016/j.ast.2013.10.001.

Crassidis JL and Mook DJ. Robust control design of an automatic carrier landing system. In: AIAA astrodynamics conference, Washington, DC, USA, 1992, AIAA-92-4619, pp.1471–1481.

Choi

Lee

et al.

Aircraft longitudinal auto-landing guidance law using time delay control scheme. T Jpn Soc Aeronaut S 2010; 53: 207–214. DOI: 10.2322/tjsass.53.207.

Duan

. Simplified brain storm optimization approach to control parameter optimization in F/A-18 automatic carrier landing system. Aerosp Sci Technol 2015; 42: 187–195. DOI: 10.1016/j.ast.2015.01.017.

10.

Lungu

. Automatic landing control using H-inf control and dynamic inversion. Proc IMechE, Part G: J Aerospace Engineering 2014; 228: 2612–2626. DOI: 10.1177/0954410014523576.

11.

Puechmorel

Tastambekov

Delahaye

. Aircraft trajectory forecasting using local functional regression in Sobolev space. Transport Res C-Emer 2014; 39: 1–22. DOI: 10.1016/j.trc.2013.11.013.

12.

Siegmund

Scheele

. Estimating errors in trajectory forecasts using ensemble predictions. J Appl Meteorol 2001; 40: 1223–1232.

13.

Khalil

Rui

Hendy

. Discrete time transfer matrix method for projectile trajectory prediction. J Aerospace Eng 2015; 28: 1–9. DOI: 10.1061/(ASCE)AS.1943-5525.0000381.

14.

Tian

Dai

. Research on the relationship between mishap risk and time margin for control: a case study for carrier landing of aircraft. Cogn Tech Work 2014; 16: 259–270. DOI: 10.1007/s10111-013-0262-y.

15.

Tian J and Zhao TD. Controllability-involved risk assessment model for carrier-landing of aircraft. In: Annual reliability and maintainability symposium (RAMS). Reno, NV, USA, 2012, pp.1–5.

16.

Maria

Gomez

Mijares

et al.

The development of probabilistic models to estimate accident risk (due to runway overrun and landing undershoot) applicable to the design and construction of runway safety areas. Saf Sci 2011; 49: 633–650. DOI: 10.1016/j.ssci.2010.09.020.

17.

Trucco

Chiara

. Topological risk mapping of runway overruns: a probabilistic approach. Reliab Eng Syst Safe 2015; 142: 433–443. DOI: 10.1016/j.ress.2015.06.006.

18.

Vian

Moore

. Trajectory optimization with risk minimization for military aircraft. J Guid Control Dynam 1989; 12: 311–317.

19.

De RF and Buskens C. Emergency flight replanning for minimum loss of life risk using a decoupled trajectory optimization approach. In: AIAA 2013 aviation technology integration, and operations conference, Washington, DC, USA, 2013, AIAA 2013-4238, pp.1–14.

20.

Stephen G, McCauley and Nagatit MG. An aircraft trajectory model for risk minimization. In: AIAA 33rd aerospace sciences meeting and exhibit, Washington, DC, USA, 1995, AIAA-95-0340, pp.1–10.

21.

ArkunY . Quasi-min-max MPC algorithms for LPV systems. Automatica 2000; 36: 527–540. DOI: 10.1016/S0005-1098(99)00176-4.

22.

Park

Kim

Sugie

. Output feedback model predictive control for LPV systems based on quasi-min–max algorithm. Automatica 2011; 47: 2052–2058. DOI: 10.1016/j.automatica.2011.06.015.

23.

Reble M, Quevedo ED and Allgower F. Improved stability conditions for unconstrained nonlinear model predictive control by using additional weighting terms. 51st IEEE annual conference on decision and control (CDC), Maui, HI, USA, IEEE, 2012, pp.2625–2630.

24.

Pluymers B, Suykens J and De Moor B. Linear MPC with time-varying terminal cost using sparse convex combinations and bisection search. 43rd IEEE conference on decision and control, San Diego, CA, USA, 2004, pp.2029–2034.

25.

Hovd

Olaru

. Piecewise quadratic Lyapunov functions for stability verification of approximate explicit MPC. Model Ident Control 2010; 31: 45–53. DOI: 10.4173/mic.2010.2.1.

26.

Lee

Won

. Model predictive control for linear parameter varying systems using a new parameter dependent terminal weighting matrix. IEICE T Fund Electr 2006; E89A: 2166–2172. DOI: 10.1093/ietfec/e89–a.8.2166.

27.

Eastburg

. Natops landing signal officer manual. Navair 00-80T-104, Patuxent River: American Navy Naval Air Systems, 2007.

28.

Chakraborty A. Linear and nonlinear analysis of susceptibility of F/A-18 flight control laws to the falling leaf mode. PhD Dissertation, Aerospace Engineering and Mechanics, University of Minnesota, MN, 2010.

29.

Chakraborty A, Seiler P and Balas GJ. Applications of linear and nonlinear robustness analysis techniques to the F/A-18 flight control laws. In: AIAA guidance, navigation, and control conference, Washington DC, USA, 2009, AIAA 2009-5675, pp.10–13.

30.

Chakraborty

Seiler

Balas

. Susceptibility of F/A-18 Flight controllers to the falling-leaf mode: nonlinear analysis. J Guid Control Dynam 2011; 34: 73–85.

31.

Wang

Zhu

Zhang

et al.

Modeling pilot behaviors based on discrete-time series during carrier-based aircraft landing. J Aircraft 2016; 53: 1922–1931. DOI: 10.2514/1.C033721.

32.

Sweger JF. Design specifications development for unmanned aircraft carrier landings: a simulation approach. US Naval Academy, Trident Scholar Project Rept. 316, Annapolis, MD.